Control of echo cancellation filters

ABSTRACT

Coefficients of an adaptive filter representative of an acoustic channel between an emitted acoustic signal and a microphone signal are determined and smoothed in time. An echo is then estimated by filtering the emitted acoustic signal with the smoothed coefficients. Properties of the estimated echo and of the microphone signal are estimated. The echo cancellation filter is controlled as a function of a comparison between the properties of the estimated echo and those of the microphone signal so as to take into account the potential presence of a signal other than an echo signal in the microphone signal.

This application claims priority from French Application FR 06 11485,filed Dec. 28, 2006, which is hereby incorporated by reference in itsentirety.

BACKGROUND OF THE INVENTION

The present invention relates to the control of echo cancellationfilters.

In numerous communication systems and equipment, a problem arises withecho, that is to say situations in which an acoustic signal is emittedand simultaneously, totally or partially acquired, and then played backto the initial emitter in the form of an echo.

This type of situation occurs during communications implementingequipment comprising a loudspeaker for emitting an acoustic signal and amicrophone situated within range of the loudspeaker such as compact or“hands-free” type equipment. On account of the proximity between theloudspeaker and the microphone, the microphone is liable to acquire thesignal emitted by the loudspeaker. Thus, a distant talker hears his ownvoice delayed by the lag introduced by the communication chain.

In order to alleviate this problem, the terminals use echo cancellationfilters. In a general manner, an echo is estimated on the basis of thesignal emitted and is deducted from the microphone signal.

In practice, this is often carried out by adaptive filters applied tothe microphone signal. In a general manner, adaptive filtering consistsin giving an expression for the evolution of coefficients of the filterover time, this expression having to meet a convergence criterion.Several algorithms are used in echo cancellation such as, for example,the so-called LMS (Least Mean Square) or NLMS (Normalized LMS)algorithms or other algorithms that are well known to a person skilledin the art and described in particular in the document by Simon S.Haykin, “Adaptive Filter Theory”, Prentice Hall (September 2001).

In order to suitably filter the echo and not to introduce distortion inthe signal played back, it is necessary to control the echo cancellationfilters differently when there is an echo and when there is not. Moreprecisely, it is necessary to permit modifications of the parameters ofthe filters solely in an echo-only period and it is necessary to avoidmodifying the parameters of the filters in situations where there is noecho as well as in the so-called double-talk situations, that is to saycases where the microphone signal comprises an echo component and auseful signal component.

The discrimination of these situations is a complex problem. Indeed, itis relatively simple to detect periods of echo absence on account of theabsence of signal on the loudspeaker but, it is very difficult todistinguish echo-only situations from double-talk situations. Now, theperformance of the adaptive echo cancellation algorithms depends verystrongly on the capacity to distinguish these phases.

Existing solutions are based on comparing between properties of thesignal emitted and the same properties evaluated on the microphonesignal.

An example of a conventional system is represented with reference toFIG. 1 in which a terminal 2 is schematically represented. Acousticsignals are conveyed to this terminal in a conventional manner, forexample, by Hertzian wave or with the aid of any appropriatecommunication network.

The terminal receives a signal x(n) from the network such as a speechsignal. This signal x(n) is broadcast on a loudspeaker 6. The signalemitted by the loudspeaker 6 is transformed by the acoustic channel Hcorresponding to the environment of the terminal 2.

In the terminal 2, a microphone 8 records the local signal y(n),composed of a useful signal pu(n) corresponding for example to thespeech signal emitted by a talker, added with a part of the soundemitted by the loudspeaker: the acoustic echo. This echo is the resultof the convolution of the signal broadcast by the loudspeaker 6 with theacoustic channel H and depends on the dimensions of the terminal, thematerials used, the environment and other parameters.

The signal y(n) acquired by the microphone 8 is then returned to anadaptive echo cancellation filter 10. This filter 10 is used to generatean estimated echo {circumflex over (z)}(n) which is deducted from themicrophone signal in a mixer 12.

In the example described, the terminal 2 comprises a conventionalfeedback loop from the mixer 12 so that the coefficients of the filter10 are modified in such as way as to decrease the difference between theecho and the microphone signal.

The adaptive filter 10 is denoted Ĥ_(L) and is a filter of length L,whose coefficients {ĥ_(i)(n)}_(i=0, . . . , L-1) are adapted over timeand indexed by the temporal index n. This filter generates thepseudo-echo {circumflex over (z)}(n). The residual echo e(n) resultsfrom subtracting {circumflex over (z)}(n) from the microphone signaly(n) in the mixer 12. We then have the following expressions:

${\hat{z}(n)} = {\sum\limits_{i = 0}^{L - 1}{{{\hat{h}}_{i}(n)} \times \left( {n - i} \right)}}$e(n) = y(n) − ẑ(n)

In the example, a so-called LMS algorithm is used with as criterion theminimization of the power of the residual echo according to thefollowing equation:Ĥ _(L)(n)=Ĥ _(L)(n−1)+μ·e(n)·X(n)

In this equation Ĥ_(L)(n)=[ĥ₀(n), ĥ₁(n), . . . , ĥ_(L-1)(n)]^(T) is thevector of the L coefficients of the adaptive filter of the instant n,and X(n)=[x(n), x(n−1), . . . , x(n−L+1)]^(T) is the vector of the lastL samples of the signal emitted to the loudspeaker 6. The term μ is afactor called the “adaptation step size” which controls the speed ofconvergence.

The role of μ is important in controlling the stability of the filter.In the echo-only situations, the filter may be adapted in such a way asto converge speedily. In the absence of an echo, the adaptation of thecoefficients is not desirable since this may lead to mismatch of theadaptive filter, and finally to perceptible rises in echo. Likewise, assoon as the local talker is active, whether it be in a speech-only ordouble-talk situation, it is appropriate to freeze the adaptation of theecho cancellation filter 10.

In the converse case, the filter 10 seeks to suppress the useful speechand becomes maladapted. In addition to the risks of filter divergence,this leads to strong degradations of the useful signal and to thereappearance of echo, or even to its amplification.

The terminal 2 also comprises a module 14 for controlling the filter 10,also called the double-talk detection module or DTD. This module 14analyses the signals x(n) and y(n) so as to extract a decision whichmakes it possible to freeze the adaptation of the filter 10, inparticular in a period of double-talk.

The system described with reference to FIG. 1 uses a direct comparisonof the signals emitted and received. This does not however allow optimalcontrol on account of the modifications induced by the acoustic channelH.

In order to improve the detection of double-talk situations, certainmethods of controlling adaptive echo cancellation filters analyse theproperties of the channel. Such is the case in particular for thedocument P. Ahgren, “On system identification and acoustic echocancellation”, Thesis UPPSALA Universitet (April 2004) which uses twofilters Ĥ_(L) ¹ and Ĥ_(L) ². A diagram of such a system is representedin FIG. 2.

In this figure, the elements similar to those described with referenceto FIG. 1 bear the same reference numerals. Depicted are the terminal 2with the adaptive filter 10 and the mixer 12 as well as the loudspeaker6 and the microphone 8, separated by the acoustic channel H.

In this embodiment, the double-talk detection module 14 is alsodepicted. The terminal 2 comprises however a second adaptive filter 16.The filter 10 is situated upstream of the double-talk detection module14 whereas the filter 16 is situated downstream of the module 14, withrespect to the direction of processing of the microphone signal.

The filter 10 is continuously adapted by virtue of the use of a negativefeedback loop implemented in a conventional manner to reduce theresidual calculated by the mixer 12 between the pseudo-echo and themicrophone signal.

The filter 16 is also adapted according to a feedback loop, thisadaptation being driven by the decision of the double-talk detectionmodule 14. If the module 14 detects the presence of local speech, it maybe decided, for example, to freeze the filter 16 or any other softdecision making it possible to slow down the adaptation according to thedegree of probability of the presence of local speech. It is the filter16 which serves to estimate the echo {circumflex over (z)}₂ (n) which isthen subtracted from the microphone signal by a mixer 18.

In an echo-only period, when the acoustic channel H does not varyabruptly, the evolution of the coefficients of the filter 10 slows downin tandem with the convergence of the coefficients. As soon asdouble-talk is present, the coefficients of the filter 10, which iscontinuously adapted, are greatly modified by the presence of usefulspeech.

When these coefficients vary quickly and strongly, the probability ofbeing in a double-talk situation is considerable.

For reasons of simplicity of implementation, the variance is calculatedonly for the largest value of the coefficients of the adaptive filterĤ_(L) ¹:

$\gamma = {\max\limits_{h_{i}^{1}}{\left( {h_{i}^{1},{\ldots\mspace{11mu} h_{L}^{1}}} \right)/}}$where h_(i) ¹ signifies that these are the coefficients of thecontinuously adapted filter 10. This document proposes to compare thevariance of γ with a fixed threshold. Thus, in the presence of echo, astrong variance signals the presence of a useful speech signal, andconsequently, a potential double-talk period. Therefore, thecoefficients h_(i) ¹ of the filter 10 are no longer copied over to thesecond filter 16 whose evolution is frozen.

Such a system does not however make it possible to differentiate betweena variation of the acoustic channel and the appearance of a double-talksituation. These phenomena have the same impact on the evolution of thecoefficients of the adaptive filter 16 that is used to calculate thepseudo-echo which is subtracted from the microphone signal.

Thus, the existing methods and systems are not entirely satisfactory asregards the control of echo cancellation filters in particular, owing tothe imperfect detection of double-talk situations.

An object of the invention is to improve this situation by virtue of amethod and a device for controlling echo cancellation filters.

SUMMARY OF THE INVENTION

A method of controlling an echo cancellation filter is proposed,comprising:

-   -   determining coefficients of an adaptive filter representative of        an acoustic channel between an acoustic signal emitted and a        microphone signal    -   temporal smoothing of the coefficients of said adaptive filter;    -   determining an estimated echo by filtering the acoustic signal        emitted with said smoothed coefficients;    -   estimating properties of said estimated echo;    -   estimating the same properties on the microphone signal;    -   comparing between said properties of the estimated echo and said        properties of the microphone signal so as to evaluate the        presence of a signal other than an echo signal in the microphone        signal; and    -   controlling the filter for cancelling an echo in the microphone        signal, as a function of said comparison.

The use of properties of the estimated echo allows a more relevantanalysis for evaluating the presence of an echo and the presence of asignal other than an echo signal and therefore makes it possible todetect the potential situations of double-talk.

Furthermore, the method compares the signals emitted and received whiletaking account of the acoustic channel.

The method also makes it possible to obtain directly the estimation ofthe acoustic channel and the smoothing of the variations over time ofthis channel renders the method robust to fast variations of theacoustic channel.

In a particular embodiment, the said estimating of properties of theestimated echo and the said estimating of properties of the microphonesignal each comprise an auto-regressive modelling. The use of anauto-regressive model makes it possible to track the evolution of thesignals in an effective manner.

Advantageously, the auto-regressive modelling of the microphone signalcomprises the application, to the microphone signal, of the parametersof the auto-regressive modelling of the estimated echo. Thus, it is notnecessary to undertake the identification of the parameters of the ARmodel of the microphone signal and the comparison of the properties isdone by evaluating the relevance of the model of the estimated echoapplied to the microphone signal.

In a particular embodiment, the said estimating of properties of theestimated echo furthermore comprises the determining of a predictionresidual arising from a prediction of the estimated echo by thecorresponding auto-regressive model and the said estimating ofproperties of the microphone signal furthermore comprises thedetermining of a prediction residual arising from a prediction of themicrophone signal by the auto-regressive model. These residuals are thusdirectly comparable and make it possible to characterize the similitudebetween the estimated echo and the microphone signal. For example, thedegree of similitude may be evaluated by comparing the energies of theprediction residuals.

In a variant, the comparing of the properties of the estimated echo andthe properties of the microphone signal comprises the forming of anindicator representing the probability that the microphone signalcomprises solely an echo signal corresponding to the signal emitted.This indicator thus makes it possible to detect the potential periods ofdouble-talk.

Advantageously, the said controlling of a cancellation filter comprisesthe supervising of the variations of the said filter as a function ofthe presence of a signal other than the echo signal in the microphonesignal. This makes it possible in particular to freeze the adaptivefilters in the event of potential double-talk.

The invention also provides a corresponding program as well as a devicefor controlling an echo cancellation filter and a system as well as aterminal comprising such a device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 and 2, already commented on, represent diagrams of devices ofthe prior art.

FIG. 3 is a flowchart of a method according to the invention.

FIG. 4 is a diagram of a device according to the invention.

FIGS. 5A to 5D represent the evolution of certain signals in the courseof the method of the invention.

DESCRIPTION OF EMBODIMENTS

With reference to FIG. 3, the method of the invention will now bedescribed in a general manner.

This method is implemented at the level of a terminal emitting anacoustic signal x(n) through a loudspeaker. This terminal also receivesanother acoustic signal y(n), termed the microphone signal or receivedsignal, and liable to comprise an echo of the acoustic signal emitted.

The method begins with a step 20 of determining an estimated echo whichcomprises an estimation 21 of the acoustic channel H.

In the embodiment described, the step 21 comprises an estimation 22 of afirst pseudo-echo on the basis of the acoustic signal emitted x(n). Thisestimation 22 comprises, for example, the application of an adaptivefilter to the signal emitted and the modification of the parameters ofthis filter by a negative feedback loop. The negative feedback loop isimplemented in a conventional manner, on the basis of the microphonesignal, so as to reduce the residual between the microphone signal andthe pseudo-echo.

The estimation 22 thus makes it possible to obtain a first estimation ofthe acoustic channel H by way of the coefficients of the adaptivefilter.

The estimation 22 is followed by the estimation of the mean acousticchannel, that is to say of a temporal smoothing 24 of the variations ofthe coefficients of the adaptive filter. Thus, on completion of step 22,an evaluation of the mean acoustic channel is available.

The method continues with an estimation 26 of a second pseudo-echo,carried out on the basis of the acoustic signal emitted and thetime-smoothed coefficients of the first adaptive filter.

More precisely, this second pseudo-echo is obtained by applying thefilter consisting of the smoothed coefficients to the emitted signalx(n). An echo calculated with a time-smoothed estimation of the acousticchannel H is thus obtained.

The method then comprises a step 28 of estimating properties of thesecond pseudo-echo as well as a step 30 of estimating the sameproperties on the microphone signal. In the embodiment described, theproperties used are acoustic properties obtained by auto-regressivemodels.

The properties of the second pseudo-echo and of the microphone signalare compared in the course of a step 32 so as to form an indicator ofthe probability that the situation is a pure-echo situation, that is tosay that the microphone signal comprises an echo component only. Thisindicator is used to control an echo cancellation filter during a step34.

With reference to FIG. 4, the details of a device implementing themethod of the invention will now be described.

As in the cases described with reference to FIGS. 1 and 2, this deviceor terminal 2 comprises the loudspeaker 6 separated from the microphone8 by the acoustic channel H.

Furthermore, the terminal 2 comprises an echo cancellation filter 36receiving as input the emitted signal x(n) as well as the receivedsignal y(n).

The terminal comprises a module 38 for controlling the echo cancellationfilter, or double-talk detection module (DTD).

This module 38 comprises a unit 40 for determining an estimated echowhich receives as input the signal emitted x(n) destined for theloudspeaker 6 as well as the microphone signal y(n) acquired by themicrophone 8.

In the embodiment described, the unit 40 comprises firstly an estimator42 of the first pseudo-echo implementing the step 22 describedpreviously. This estimator 42 carries out an adaptive filtering the aimof which is to estimate the transfer function of the acoustic channel Hand to track its variations over time. Its implementation relies on anadaptive algorithm of conventional type such as a so-called LMS (LeastMean Square) or NLMS (Normalized LMS) algorithm, an APA (AffineProjection Algorithm) or any other equivalent algorithm.

Advantageously, the estimator 42 comprises a feedback loop aimed atreducing the difference between the microphone signal y(n) and the firstpseudo-echo. This difference is called the residual echo and iscalculated by a mixer 43 in a conventional manner.

In this implementation, the adaptive algorithm chosen for the estimator42 is an NLMS, whose adaptation equation is the following, with the samenotation as previously:

${H_{1}\left( {n + 1} \right)} = {{H_{1}(n)} + {\mu \cdot \frac{X(n)}{{X(n)}^{T}{X(n)}} \cdot {{e_{1}(n)}.}}}$

Thus, a first pseudo-echo z₁(n) is generated by filtering the emittedsignal x(n) with the adapted filter H₁. As indicated previously inrespect of adaptive filters in general, H₁(n)=[h_(1,0)(n), h_(1,1)(n), .. . , h_(1,L-1)(n)]^(T) denotes the vector of the L coefficients of thefilter H1 at the instant n. Consequently, the pseudo-echo is expressedby:

${z_{1}(n)} = {\sum\limits_{i = 0}^{L - 1}{{h_{1,i}(n)} \times {\left( {n - i} \right).}}}$

The residual echo e₁(n) delivered by the mixer 43 is equal to themicrophone signal from which the pseudo-echo is subtracted:e ₁(n)=y(n)−z ₁(n).

Advantageously, the management of the adaptation of the filter of theestimator 42 involves an expression for a variable adaptation step sizeμ which makes it possible to manage the adaptation of the filter. Thus,this technique proposes that the convergence term μ be made to evolve inan interval [μ_(min), μ_(max)] as a function of the energy levels of theemitted signal x(n) and of the microphone signal y(n) and according tothe following behaviour:

-   -   μ(n)→μ_(max) in an echo-only period, so as to make Ĥ_(L)        converge,    -   μ(n)→μ_(min) in a useful speech-only phase, so as to stabilize        Ĥ_(L),    -   μ(n)ε[μ_(min), μ_(max)] in a double-talk phase. It is necessary        that μ(n)→μ_(min) if the useful speech is predominant relative        to the echo and that μ(n)→μ_(max) in the converse case.

To define an expression which satisfies these trends, it is appropriateto make the assumption according to which the energy of the echo isglobally lower than the energy of the useful speech. An expression for μwhich satisfies the desired behaviour is defined below, where (a,b,c)are parameters which depend on the properties of the terminal. The powerof the signal considered at the instant n is denoted σ²(n). Thus μ isexpressed by:

${\mu(n)} = \frac{a \cdot {\sigma_{HP}^{2}(n)}}{{b \cdot {\sigma_{HP}^{2}(n)}} + {c \cdot {\sigma_{Micro}^{2}(n)}}}$

As long as the hypothesized assumption holds, the contribution of theecho in σ_(Micro) ² is small relative to that of the useful speech andthis expression makes it possible to obtain the desired behaviour.

In a double-talk situation, the adaptation of the filter H1 must bedisabled and the term μ is fixed at 0.

Of course, other adaptation management laws are also possible such asthose described in the document P. Scalart, P. Duhamel and A. Benamar,Process and device for adaptive identification and adaptive echocanceller relating thereto, U.S. Pat. No. 5,734,715 (March 1998).

The unit 40 also comprises an integrator 44 which implements thetemporal smoothing step 24.

This integrator 44 carries out a temporal smoothing of the coefficientsof the filter of the estimator 42. Specifically, for a given acousticconfiguration, it is considered that the majority of the energy due tothe acoustic coupling originates from the direct path between theloudspeaker 6 and the microphone 8 as well as the first reflectionsrelated to the structure of the terminal. Consequently, thesemodifications, which correspond to the acoustic channel H, arerelatively stable.

Also, when the filter H1 of the estimator 42 is undergoing adaptation,its coefficients evolve a great deal when it starts from a maladaptedstate so as to attain the adapted state, and evolve very little when thefilter has converged.

The smoothing makes it possible to obtain filtering coefficientscorresponding to an estimation of the acoustic channel H that is lesssensitive to disturbances than that obtained by the coefficients of thefilter H1, in particular upon the appearance of speech at the level ofthe terminal which causes mismatch of the coefficients of the echocancellation filter.

In the example, this temporal smoothing is obtained through thefollowing recursive expression:H ₂(n)=α·H ₂(n−1)+(1−α)H ₁(n)

The smoothing quantity α is chosen constant and equal to α=0.96. Thisvalue is judged sufficient to ensure a compromise between the trackingof the variations of the acoustic channel and the occurrences ofdouble-talk.

Of course, the temporal integration is not limited to this exponentialsmoothing and other expressions may be used.

The estimator 42 and the integrator 44 thus deliver an estimation of themean acoustic channel, implementing step 21 of the method describedpreviously.

The smoothed coefficients are used in a filter H2 of an estimator 46 toform a second pseudo-echo which implements step 26 described previously.

This estimator 46 generates, on the basis of the filter H2H₂(n)=[h_(2,0)(n), h_(2,1)(n), . . . , h_(2,L-1)(n)]^(T) whosecoefficients are smoothed, the second pseudo-echo z₂(n) according to thefollowing expression:

${z_{2}(n)} = {\sum\limits_{i = 0}^{L - 1}{{h_{2,i}(n)} \times \left( {n - i} \right)}}$

The second pseudo-echo z₂(n) forms the output of the unit 40, that is tosay the estimated echo for the emitted signal x(n).

Next, the module 38 comprises a unit 48 for estimating properties of thesecond pseudo-echo, such as spectral envelope properties.

The unit 48 implements step 28 described previously and comprises, inthe example, a calculation of an auto-regressive model, termed an ARmodel which makes it possible to estimate the spectral envelope of thesignal. Alternatively, the unit 28 implements a calculation of thefundamental frequency of the second pseudo-echo or any other proceduremaking it possible to extract a property specific to the secondpseudo-echo.

These properties being determined on the basis of the pseudo-echo andnot on the basis of the emitted signal x(n), they take account of theestimation of the acoustic channel H. More precisely, these propertiestake account of the estimation of the spectral coloration of theacoustic signal.

Furthermore, the use of two pseudo-echoes makes it possible to dispensewith instantaneous modifications and to obtain a robust estimation ofthe acoustic channel even during spikes of high power.

In the example, the unit 48 calculates an AR model of order P of thesecond pseudo-echo z₂(n) according to the following equation:

${e_{z\; 2}(n)} = {{z_{2}(n)} - {\sum\limits_{i = 1}^{P}{\alpha_{i}{z_{2}\left( {n - i} \right)}}}}$e_(z2)(n) is the prediction residual, and the coefficients(α_(i))_(1≦i≦P) are calculated with the aim of minimizing the powerE{e_(z2)(n)²} of e_(z2)(n).

In the case where z₂(n) is a speech signal, it is at best stationaryover short periods of a few tens of milliseconds. The coefficients(α_(i))_(1≦i≦P) must therefore be regularly updated. Several algorithmsmake it possible to calculate these coefficients, including adaptivefiltering algorithms (LMS—NLMS or Block NLMS) or solving the Yule-Walkerequation with the Levinson-Durbin algorithm. The so-called Block NLMSand Levinson-Durbin algorithms perform the calculation on frames duringwhich the signal is assumed to be stationary. For example, the unit 48uses the Levinson-Durbin algorithm on frames of 20 ms.

For a sampling frequency equal to 8000 Hz, it is accepted that an orderp of less than 10 is generally sufficient to model the spectralenvelope.

Thus, the signal e_(z2)(n) and the coefficients (a_(i))_(1≦i≦P) are bothrepresentative of the properties of the second pseudo-echo z₂(n) andeither one may be used. In the example, the signal e_(z2)(n) is used.

Furthermore, the module 38 comprises a unit 50 for estimating the sameproperties on the microphone signal y(n).

This module 50 implements step 30 of the method described previously byperforming, on the microphone signal, the same operations as thosecarried out by the module 48.

Insofar as the AR model calculated by the module 48 is representative ofthe spectral envelope of the estimated echo signal, an advantageousimplementation consists in reusing this model so as to apply it to thesignal y(n) as represented in FIG. 4. Thus, the module 50 deliversproperties of the acoustic signal in the form of a residual e_(y)(n)according to the following expression:

${e_{y}(n)} = {{y(n)} - {\sum\limits_{i = 1}^{P}{a_{i}{{y\left( {n - i} \right)}.}}}}$

Consequently, if the microphone signal y(n) contains echo only, the ARmodel will be well adapted and the residuals e_(z2)(n) and e_(y)(n) willbe “comparable”. On the other hand, if another signal, such as noise oruseful speech, is added to the microphone signal, the AR model will notbe adapted and the residuals will be “different”.

The residuals e_(z2)(n) and e_(y)(n) are then transmitted to a unit 52for comparison and control of the echo cancellation filter.

This unit 52 therefore receives as input the information representativeof the properties of the second pseudo-echo and of the microphone signalso as to determine the probability of the presence of a useful signal,that is to say the probability that there is a signal other than an echosignal in the microphone signal.

When the microphone signal is composed of echo alone, the properties ofthe estimated echo and of the microphone signal are theoreticallysimilar. On the other hand, in the presence of an additional signal atthe level of the terminal, such as for example noise or useful speech tobe transmitted, the properties calculated on the microphone signal areno longer comparable to those calculated on the second pseudo-echo.

Depending on the type of properties used, one or more analysis rulesmake it possible to determine whether a signal other than echo ispresent in the microphone signal. If such is the case, a period liableto be a double-talk period is detected. The unit 52 then controls theecho cancellation system so as, in particular, to freeze the adaptationof the echo cancellation filter 36 to avoid any maladaptation.

As indicated previously, in the example, the unit 52 takes as input theresidual signals e_(z2)(n) and e_(y)(n) so as to determine the presenceof double talk. In this implementation, the unit 52 is adapted forcomparing the powers E{e_(z2)(n)²} and E{e_(y)(n)²} of these residuals.These quantities may be estimated in various ways and in particular, byexponential smoothing which is a technique that is inexpensive in termsof calculation time, according to the following equations:Estimation of E{e _(z2)(n)²}: σ_(e) _(z2) ²(n)=λ·σ_(e) _(z2)²(n−1)+(1−λ)·e _(z2)(n)²Estimation of E{e _(y)(n)²}: σ_(e) _(y) ²(n)=λ·σ_(e) _(y) ²(n−1)+(1−λ)·e_(y)(n)².

In these equations, λ is taken close to 1, for example equal toλ=0.9961, this corresponding, for a sampling frequency of 8000 Hz, to atime constant of 32 ms.

In an echo-only period, the powers of the two residual signals e_(z2)(n)and e_(y)(n) are comparable.

In a double-talk period, the echo signal spectral envelope, added to theuseful speech signal, is different from that of the echo only. Also, theresidual e_(y)(n) contains energy in the zones that are not modelled bythe AR model defined on the basis of the estimated echo, so that thepower of e_(y)(n) must be greater than that of e_(z2)(n).

Thus, the comparison may be performed on the basis of the ratio η(n)between the powers σ_(e) _(z2) ²(n) and σ_(e) _(y) ²(n), the expressionfor which at the instant n is as follows:

${\eta(n)} = \frac{\sigma_{e_{z\; 2}}^{2}(n)}{\sigma_{e_{y}}^{2}(n)}$

A simple rule for comparing η(n) with a threshold T makes it possible todetect the periods that are liable to be double-talk periods, accordingto the following rule:if η(n)<T

risk of double-talkelse

absence of double-talk

The value of T may be fixed empirically and must be determined as afunction of a tolerated false alarm rate. In this example, the detectionthreshold is fixed but an adaptive rule may be envisaged. It is alsopossible to envisage a system for maintaining the decision of risk ofdouble-talk (“hangover”) as is conventionally encountered in voiceactivity detectors.

Furthermore, a timeout makes it possible to short-circuit the controlunit 52 during the first seconds of adaptation of the filter, so as toavoid freezing the adaptation. This timeout is active at the start ofprocessing and after any supervising of the adaptive filter whichinvolves setting the step size to zero or resetting the coefficients tozero.

The command is emitted by the unit 52 to the echo cancellation filter36. This filter may be any type of filter requiring to be modifieddepending on whether or not the situation may be a double-talksituation. This filter may or may not be adaptive, and may or may notcomprise a non linear processing.

In the example, when the module 52 has decided an absence of localspeech, the coefficients H1 of the filter 42 which are continuouslyadapted are simply transmitted to the filter 36. Conversely, when themodule 52 has detected a presence of local speech, the filter 36 is notupdated and the values of its coefficients are frozen.

Of course, it is also possible to control the filter 36 otherwise and inparticular to use an adaptive filter that is independent of the filters42 and 46, whose adaptation is frozen or permitted according to thedecision of the double-talk detection module 52.

FIGS. 5A to 5D represent results obtained by virtue of the method ofFIG. 3 and the system of FIG. 4 within the framework of the acousticecho cancellation applied to spoken communication.

The sampling frequency is 8000 Hz. The acoustic channel is known and oflength 512 points, the adaptive filter 42 is of length L=256. Theecho-to-noise ratio, that is to say the ratio of the power of the echoto the power of the useful speech, is of the order of −3 dB.

The curve of FIG. 5A represents the echo signal z(n), the curve of FIG.5B represents the useful speech pu(n) and the curve of FIG. 5Crepresents the microphone signal y(n).

The curve represented in FIG. 5D represents the ratio η used to detectpotential periods of double-talk. In FIGS. 5C and 5D, the hatched zonescorrespond to the double-talk periods.

The curve of FIG. 5D shows indeed that the quantity η is close to 1 inan echo-only period. In the periods in which echo is absent, theevolution of η is not important and has been masked in thisrepresentation. In a double-talk period, the quantity η decreases andbecomes appreciably less than 1, readily making it possible to detectthe presence of a signal other than the echo at the level of theterminal 2. Of course, the sensitivity of the system can beparameterized via the threshold. In this case, it is for examplepossible to choose a threshold value of 0.5, which entails setting theadaptation step size to zero during the double-talk periods:if η(n)<0.5

potential double-talkelse

potential pure echo

This criterion makes it possible to detect the most energetic potentialzones of double-talk and therefore the ones that are most liable to giverise to divergence of the adaptive filter.

Of course, other embodiments are also possible. In particular, in avariant, the determination of the estimated echo implements a singleadaptive filtering stage with no temporal smoothing. In this case, theproperties of the estimated echo are evaluated directly on the outputsignal from the adaptive filter.

It is also possible to obtain the mean acoustic channel otherwise thanby temporal smoothing. For example, it is possible to use the leastsquares procedure over a considerable time window to obtain anevaluation of the acoustic channels transfer function which is alreadyaveraged over time.

Moreover, the invention may be implemented by means of software or partsof software comprising code instructions which, when they are executedby a computer, give rise to the implementation of the method of theinvention. Such software may in particular be stored in the memory of amicroprocessor or of a digital signal processor (DSP).

It is also possible to use a dedicated component such as a programmedcomponent intended to be integrated into a device such as a telephone.

The invention claimed is:
 1. Method of controlling an echo cancellationfilter comprising: determining coefficients of an adaptive filterrepresentative of an acoustic channel between an emitted acoustic signaland a microphone signal; smoothing in time the coefficients of saidadaptive filter; determining an estimated echo by filtering the emittedacoustic signal with the smoothed coefficients; estimating properties ofsaid estimated echo; estimating the same properties on the microphonesignal; comparing said properties of the estimated echo and saidproperties of the microphone signal so as to evaluate the presence of asignal other than an echo signal in the microphone signal; andcontrolling the echo cancellation filter for cancelling an echo in themicrophone signal, as a function of the comparison; wherein the steps ofestimating properties of the estimated echo and of estimating propertiesof the microphone signal each comprise an auto-regressive modeling. 2.Method according to claim 1, wherein the auto-regressive modeling of themicrophone signal comprises applying parameters of the auto-regressivemodeling of the estimated echo to the microphone signal.
 3. Methodaccording to claim 1, wherein the step of estimating properties of theestimated echo further comprises determining a prediction residualarising from a prediction of the estimated echo by the autoregressivemodel determined for said estimated echo, and the step of estimatingproperties of the microphone signal further comprises determining aprediction residual arising from a prediction of the microphone signalby the auto-regressive model determined for said microphone signal. 4.Method according to claim 1, wherein the step of comparing theproperties of the estimated echo and the properties of the microphonesignal comprises forming an indicator representing a probability thatthe microphone signal comprises only an echo signal corresponding to theemitted acoustic signal.
 5. Method according to claim 1, wherein thestep of controlling the echo cancellation filter comprises supervisingvariations of said echo cancellation filter as a function of thepresence of a signal other than the echo signal in the microphonesignal.
 6. A computer program product for a device for controlling anecho cancellation filter, the program product comprising instructions orportions of instruction for controlling the following steps whenexecuted by a computer of said device: determining coefficients of anadaptive filter representative of an acoustic channel between an emittedacoustic signal and a microphone signal; smoothing in time thecoefficients of said adaptive filter; determining an estimated echo byfiltering the emitted acoustic signal with the smoothed coefficients;estimating properties of said estimated echo; estimating the sameproperties on the microphone signal; comparing said properties of theestimated echo and said properties of the microphone signal so as toevaluate the presence of a signal other than an echo signal in themicrophone signal; and controlling the echo cancellation filter forcancelling an echo in the microphone signal, as a function of thecomparison; wherein the steps of estimating properties of the estimatedecho and of estimating properties of the microphone signal each comprisean auto-regressive modeling.
 7. A computer program product according toclaim 6, wherein the auto-regressive modeling of the microphone signalcomprises applying parameters of the autoregressive modeling of theestimated echo to the microphone signal.
 8. A computer program productaccording to claim 6, wherein the step of estimating properties of theestimated echo further comprises determining a prediction residualarising from a prediction of the estimated echo by the auto-regressivemodel determined for said estimated echo, and the step of estimatingproperties of the microphone signal further comprises determining aprediction residual arising from a prediction of the microphone signalby the autoregressive model determined for said microphone signal.
 9. Acomputer program product according to claim 6, wherein the step ofcomparing the properties of the estimated echo and the properties of themicrophone signal comprises forming an indicator representing aprobability that the microphone signal comprises only an echo signalcorresponding to the emitted acoustic signal.
 10. A computer programproduct according to claim 6, wherein the step of controlling the echocancellation filter comprises supervising variations of said echocancellation filter as a function of the presence of a signal other thanthe echo signal in the microphone signal.
 11. Device for controlling anecho cancellation filter, comprising: means for determining coefficientsof an adaptive filter representative of an acoustic channel between anemitted acoustic signal and a microphone signal; means for smoothing intime coefficients of said adaptive filter; means for determining anestimated echo by filtering the emitted acoustic signal with saidsmoothed coefficients; means for estimating properties of said estimatedecho; means for estimating the same properties on the microphone signal;means for comparing said properties of the estimated echo and saidproperties of the microphone signal so as to evaluate the presence of asignal other than an echo signal in the microphone signal; and means forcontrolling the echo cancellation filter for the microphone signal as afunction of said comparison; wherein the means for estimating propertiesof the estimated echo and the means for estimating properties of themicrophone signal each implement auto-regressive modeling.
 12. Deviceaccording to claim 11, wherein the autoregressive modeling of themicrophone signal comprises applying parameters of the auto-regressivemodeling of the estimated echo to the microphone signal.
 13. Deviceaccording to 11, wherein the means for estimating properties of theestimated echo further comprise means for determining a predictionresidual arising from a prediction of the estimated echo by theauto-regressive model determined for said estimated echo, and the meansfor estimating properties of the microphone signal further comprisemeans for determining a prediction residual arising from a prediction ofthe microphone signal by the autoregressive model determined for saidmicrophone signal.
 14. Device according to claim 11, wherein the meansfor comparing the properties of the estimated echo and the properties ofthe microphone signal comprise a generator of an indicator representinga probability that the microphone signal comprises only an echo signalcorresponding to the emitted acoustic signal.
 15. Device according toclaim 11, wherein the means for controlling the echo cancellation filterare arranged to control variations of said echo cancellation filter as afunction of the presence of a signal other than the echo signal in themicrophone signal.
 16. Communication system comprising at least oneterminal having a loudspeaker for emitting an acoustic signal, amicrophone for sensing a microphone signal, an echo cancellation filterfor compensating an acoustic channel between the emitted acoustic signaland the microphone signal, and a device for controlling the echocancellation filter, wherein the device for controlling the echocancellation filter comprises: means for determining coefficients of anadaptive filter representative of an acoustic channel between theemitted acoustic signal and the microphone signal; means for smoothingin time coefficients of said adaptive filter; means for determining anestimated echo by filtering the emitted acoustic signal with saidsmoothed coefficients; means for estimating properties of said estimatedecho; means for estimating the same properties on the microphone signal;means for comparing said properties of the estimated echo and saidproperties of the microphone signal so as to evaluate the presence of asignal other than an echo signal in the microphone signal; and means forcontrolling the echo cancellation filter for the microphone signal as afunction of said comparison; wherein the means for estimating propertiesof the estimated echo and the means for estimating properties of themicrophone signal each implement auto-regressive modeling.
 17. Acommunication terminal, comprising a loudspeaker for emitting anacoustic signal, a microphone for sensing a microphone signal, an echocancellation filter for compensating an acoustic channel between theemitted acoustic signal and the microphone signal, and a controller forthe echo cancellation filter, wherein the controller comprises: meansfor determining coefficients of an adaptive filter representative of anacoustic channel between the emitted acoustic signal and the microphonesignal; means for smoothing in time coefficients of said adaptivefilter; means for determining an estimated echo by filtering the emittedacoustic signal with said smoothed coefficients; means for estimatingproperties of said estimated echo; means for estimating the sameproperties on the microphone signal; means for comparing said propertiesof the estimated echo and said properties of the microphone signal so asto evaluate the presence of a signal other than an echo signal in themicrophone signal; and means for controlling the echo cancellationfilter for the microphone signal as a function of said comparison;wherein the means for estimating properties of the estimated echo andthe means for estimating properties of the microphone signal eachimplement auto-regressive modeling.
 18. The communication terminal asclaimed in claim 17, wherein the auto-regressive modeling of themicrophone signal comprises applying parameters of the autoregressivemodeling of the estimated echo to the microphone signal.
 19. Thecommunication terminal as claimed in claim 17, wherein the means forestimating properties of the estimated echo further comprise means fordetermining a prediction residual arising from a prediction of theestimated echo by the auto-regressive model determined for saidestimated echo, and the means for estimating properties of themicrophone signal further comprise means for determining a predictionresidual arising from a prediction of the microphone signal by theauto-regressive model determined for said microphone signal.
 20. Thecommunication terminal as claimed in claim 17, wherein the means forcomparing the properties of the estimated echo and the properties of themicrophone signal comprise a generator of an indicator representing aprobability that the microphone signal comprises only an echo signalcorresponding to the emitted acoustic signal.
 21. The communicationterminal as claimed in claim 17, wherein the means for controlling theecho cancellation filter are arranged to control variations of said echocancellation filter as a function of the presence of a signal other thanthe echo signal in the microphone signal.